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Multi-release software model based on testing coverage incorporating random effect (SDE).

Ritu Bibyan1, Sameer Anand2, Anu G Aggarwal1

  • 1Department of Operational Research, University of Delhi, New Delhi, India.

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|March 3, 2023
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Summary

This study introduces a new software reliability growth model incorporating testing coverage, random effects, and imperfect debugging. The model accurately fits failure data across multiple software releases, enhancing software quality assessment.

Keywords:
Imperfect debuggingMethod nameMulti-release softwareNon-homogeneous poisson process (NHPP)Software reliability growth modelsStochastic Differential EquationTesting Coverage incorporating SDE with Multi Release SRGMTesting coverageTesting coverage incorporating sde with multi release SRGM

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Area of Science:

  • Software Engineering
  • Reliability Engineering

Background:

  • Software reliability growth models (SRGMs) are crucial for assessing software quality.
  • Testing coverage significantly influences software reliability.
  • Software firms continuously update products, necessitating models for multi-release analysis.

Purpose of the Study:

  • To propose a novel SRGM incorporating testing coverage with random effects and imperfect debugging.
  • To address the multi-release software problem within the proposed framework.
  • To validate the model's performance using real-world software failure data.

Main Methods:

  • Developed a software reliability growth model using Stochastic Differential Equations (SDE) to handle random effects in testing coverage.
  • Incorporated imperfect debugging and explored three testing coverage functions: Exponential, Weibull, and S-shaped.
  • Validated the proposed model on a multi-release dataset from Tandem Computers.

Main Results:

  • The proposed model demonstrated a significant fit to the failure data across four software releases.
  • Performance criteria analysis confirmed the model's effectiveness in predicting software reliability.
  • The inclusion of random effects and imperfect debugging improved model accuracy.

Conclusions:

  • The developed SRGM effectively captures the dynamics of software reliability growth, considering testing coverage and random effects.
  • The model provides a valuable tool for software quality assessment, particularly in multi-release scenarios.
  • Future work can extend this model to include other software development factors.